
!pip3 install -U kaleido
import urllib, json
import plotly.graph_objects as go

url = 'https://raw.githubusercontent.com/plotly/plotly.js/master/test/image/mocks/sankey_energy.json'
response = urllib.request.urlopen(url)
data = json.loads(response.read())

fig = go.Figure(data=[go.Sankey(
    valueformat = ".0f",
    valuesuffix = "TWh",
    # Se definen los nodos
    node = dict(
      pad = 15,
      thickness = 20,
      line = dict(color = "black", width = 0.5),
      label =  data['data'][0]['node']['label'],
      color =  data['data'][0]['node']['color']
    ),
    # Add links
    link = dict(
      source =  data['data'][0]['link']['source'],
      target =  data['data'][0]['link']['target'],
      value =  data['data'][0]['link']['value'],
      label =  data['data'][0]['link']['label'],
      color =  data['data'][0]['link']['color']))])

fig.update_layout(title_text="Sankey diagram<br>Agencia de Información de Energía de EEUU", font_size=10)
fig.show()


# Se guarda como archivo en local
fig.write_image("sankey_diagram.png")

# Guardar figura en archivo HTML
fig.write_html("sankey_diagram.html")

Requirement already satisfied: kaleido in /Users/cgd/opt/anaconda3/envs/visualization/lib/python3.9/site-packages (0.2.1)


import plotly.graph_objects as go
import pandas as pd

facies_data = pd.read_csv('/Users/cgd/* VD/PEC2/facies_data.csv')
facies_data


corr_matrix = round(facies_data.corr(), 4)
corr_matrix

/var/folders/v4/74xgv0954936fxrjdx6yw9380000gp/T/ipykernel_11042/3787482836.py:1: FutureWarning:

The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.


import seaborn as sns
import matplotlib.pyplot as plt
import mpld3

# Se crea matriz de correlación usando Seaborn
heatmap = sns.heatmap(corr_matrix, annot=True, cmap='magma')
plt.title('Correlation Matrix', fontsize=15, color= 'navy', fontweight='bold')
plt.show(heatmap)

# Se obtiene figura
fig_cor = heatmap.get_figure()

# Se guarda como archivo en local
fig_cor.savefig("correlation_matrix.png")
d3fig = mpld3.fig_to_html(fig_cor)

# Guardar figura en archivo HTML
with open('correlation_matrix.html', 'w') as f:
    f.write(d3fig)


%matplotlib inline
import pandas as pd

# Se cargan los datos
csv_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'
iris_df = pd.read_csv(csv_url, header = None)
col_names = ['Sepal_Length','Sepal_Width','Petal_Length','Petal_Width','Species']

# Se guardan los datos como pandas dataframe
iris_df =  pd.read_csv(csv_url, names = col_names)
iris_df.head()


import seaborn as sns
import matplotlib.pyplot as plt
from ptitprince import half_violinplot

iris = sns.load_dataset('iris')

dy = "species"
dx = "sepal_length"
ort = "h"
pal = "Set2"

# Se grafica 
fig, ax = plt.subplots(figsize=(7, 5))
ax = half_violinplot(x=dx, y=dy, data=iris, palette=pal, bw=.2, cut=0.,
                     scale="area", width=.6, inner=None, orient=ort)
ax = sns.stripplot(x=dx, y=dy, data=iris, palette=pal, edgecolor="white",
                   size=3, jitter=1, zorder=0, orient=ort)
ax = sns.boxplot(x=dx, y=dy, data=iris, color="black", width=.15, zorder=10,
                 showcaps=True, boxprops={'facecolor': 'none', "zorder": 10},
                 showfliers=True, whiskerprops={'linewidth': 2, "zorder": 10},
                 saturation=1, orient=ort)
plt.title('Raincloud plot con Iris Species dataset', fontsize=15, color= 'navy', fontweight='bold')
plt.show()

# Se obtiene figura
fig_iris = fig.get_figure()

# Se guarda como archivo en local
fig_iris.savefig("Raincloud.png") # Convertir figura en formato HTML
d3fig = mpld3.fig_to_html(fig_iris)

# Guardar figura en archivo HTML
with open('raincloud.html', 'w') as f:
    f.write(d3fig)

	Facies	Formation	Well Name	Depth	GR	ILD_log10	DeltaPHI	PHIND	PE	NM_M	RELPOS
0	3	A1 SH	SHRIMPLIN	2793.0	77.450	0.664	9.900	11.915	4.600	1	1.000
1	3	A1 SH	SHRIMPLIN	2793.5	78.260	0.661	14.200	12.565	4.100	1	0.979
2	3	A1 SH	SHRIMPLIN	2794.0	79.050	0.658	14.800	13.050	3.600	1	0.957
3	3	A1 SH	SHRIMPLIN	2794.5	86.100	0.655	13.900	13.115	3.500	1	0.936
4	3	A1 SH	SHRIMPLIN	2795.0	74.580	0.647	13.500	13.300	3.400	1	0.915
...	...	...	...	...	...	...	...	...	...	...	...
3227	5	C LM	CHURCHMAN BIBLE	3120.5	46.719	0.947	1.828	7.254	3.617	2	0.685
3228	5	C LM	CHURCHMAN BIBLE	3121.0	44.563	0.953	2.241	8.013	3.344	2	0.677
3229	5	C LM	CHURCHMAN BIBLE	3121.5	49.719	0.964	2.925	8.013	3.190	2	0.669
3230	5	C LM	CHURCHMAN BIBLE	3122.0	51.469	0.965	3.083	7.708	3.152	2	0.661
3231	5	C LM	CHURCHMAN BIBLE	3122.5	50.031	0.970	2.609	6.668	3.295	2	0.653

	Facies	Depth	GR	ILD_log10	DeltaPHI	PHIND	PE	NM_M	RELPOS
Facies	1.0000	0.3402	-0.3443	0.3945	-0.2341	-0.3559	0.7042	0.8547	0.0685
Depth	0.3402	1.0000	-0.0641	0.1780	-0.0914	-0.0744	0.2776	0.2971	0.0014
GR	-0.3443	-0.0641	1.0000	-0.1556	0.1900	0.2483	-0.2890	-0.2811	-0.1735
ILD_log10	0.3945	0.1780	-0.1556	1.0000	-0.1176	-0.5229	0.3846	0.5193	0.0880
DeltaPHI	-0.2341	-0.0914	0.1900	-0.1176	1.0000	-0.2502	0.0115	-0.1740	0.0366
PHIND	-0.3559	-0.0744	0.2483	-0.5229	-0.2502	1.0000	-0.5734	-0.4884	-0.0345
PE	0.7042	0.2776	-0.2890	0.3846	0.0115	-0.5734	1.0000	0.6571	0.0189
NM_M	0.8547	0.2971	-0.2811	0.5193	-0.1740	-0.4884	0.6571	1.0000	0.0366
RELPOS	0.0685	0.0014	-0.1735	0.0880	0.0366	-0.0345	0.0189	0.0366	1.0000

	Sepal_Length	Sepal_Width	Petal_Length	Petal_Width	Species
0	5.1	3.5	1.4	0.2	Iris-setosa
1	4.9	3.0	1.4	0.2	Iris-setosa
2	4.7	3.2	1.3	0.2	Iris-setosa
3	4.6	3.1	1.5	0.2	Iris-setosa
4	5.0	3.6	1.4	0.2	Iris-setosa

1. Sankey diagram

2. Matrix Correlation

3. Raincloud plot